tutorial.AverageLearner.md

kernelspec	name display_name python3 python3
jupytext	text_representation extension format_name format_version jupytext_version .md myst 0.13 1.13.8

Tutorial {class}~adaptive.AverageLearner

Because this documentation consists of static html, the `live_plot` and `live_info` widget is not live.
Download the notebook in order to see the real behaviour. [^download]

:tags: [hide-cell]

import adaptive

adaptive.notebook_extension()

The next type of learner averages a function until the uncertainty in the average meets some condition.

This is useful for sampling a random variable. The function passed to the learner must formally take a single parameter, which should be used like a “seed” for the (pseudo-) random variable (although in the current implementation the seed parameter can be ignored by the function).

def g(n):
    import random
    from time import sleep

    sleep(random.random() / 1000)
    # Properly save and restore the RNG state
    state = random.getstate()
    random.seed(n)
    val = random.gauss(0.5, 1)
    random.setstate(state)
    return val

learner = adaptive.AverageLearner(g, atol=None, rtol=0.01)
# `loss < 1.0` means that we reached the `rtol` or `atol`
runner = adaptive.Runner(learner, loss_goal=1.0)

:tags: [hide-cell]

await runner.task  # This is not needed in a notebook environment!

runner.live_info()

runner.live_plot(update_interval=0.1)